MRI parametric mapping includes a variety of important techniques for interrogating tissue properties. To date, different parametric mapping techniques, such as T1 mapping, T2 mapping, T2* mapping, 4D flow, and diffusion tensor imaging, have been developed. Many of these techniques have been applied in clinical practice for disease diagnosis and risk stratification. For example, T2 mapping has been commonly used in cardiovascular MRI as an alternative for the traditional T2 weighted imaging due to its reliable performance. However, despite the large body of parametric mapping techniques, parametric mapping still remains underused in clinical practice for several reasons. In one aspect, parametric mapping needs to acquire several images in order to reconstruct the parameter map. Acquisition of multiple images often dramatically increases the imaging duration, making the technique inefficient relative to those single-contrast imaging method. In another aspect, parametric mapping often calls for a more complicated reconstruction. In yet another aspect, many acceleration strategies, such as compressed sensing, accelerate the acquisition but also cause prolonged reconstruction.
In single-contrast imaging, many acceleration methods have been proposed to accelerate the data acquisition, such as partial Fourier techniques, parallel imaging, simultaneous multi-slice (SMS) imaging, and dynamic MRI. These techniques allow k-space to be undersampled, thus accelerating data acquisition. Within the same acquisition time, these techniques can be used to increase spatial resolution, acquire multiple slices, or increase temporal resolution. A common feature of these techniques is that they explore information redundancy in some data domain to reduce amount of necessary samples in k-space. For example, parallel imaging explores the information redundancy between different coil-weighted images, SMS between different coil-weighted slices, and dynamic MRI between different motion states of the heart. In addition, these techniques are very practical. For example, all of them use Cartesian k-space sampling, and their reconstructions are all computationally and memory efficient. The practicality facilitates smooth translation of these techniques into clinical utilization.
In parametric mapping, fast acquisition methods are often based on three strategies. The first strategy is simply using parallel imaging to accelerate each image separately. However, this strategy does not leverage any redundant information between the images. Therefore, there is no extra acceleration compared to single-contrast imaging. The second strategy is to undersample k-space and fill the missing samples by constraining the parametric mapping with physical models of the signal. The third strategy is to use compressed sensing, which can be applied to both spatial domain and temporal domain. Although powerful, most current methods using these strategies suffer from challenges. In one aspect, a non-Cartesian trajectory or random trajectory is typically used in these techniques. These trajectories need careful calibration to correct for the influence of eddy currents, which is especially a problem for applications that have frequently changeable imaging views. In another aspect, the reconstruction problem under these strategies typically is more complicated and inefficient, due to usage of the compressed sensing regularization and non-Cartesian trajectories. Unlike parallel imaging or SMS that have spatially localized reconstruction, these strategies typically involve an optimization algorithm that jointly updates the entire image in every iteration. Although parallel computation based on GPU can somewhat reduce the computational burden, GPU is relatively expensive and is not used world widely.
Standard parametric mapping methods such as T1 and T2 mapping thus suffer from long scan time and low resolution, because they require sampling multiple images along the relaxation curve. There remains a need in the art for methods and techniques that allow for high resolution and more efficient MRI parametric mapping with Cartesian k-space sampling and efficient reconstruction. Such methods should yield improved efficiency and resolution in MRI parameter mapping with clinically preferred reconstruction time. The present invention meets this need.